| NTIRE 2023 challenge on efficient super-resolution: Methods and results Y Li, Y Zhang, R Timofte, L Van Gool, L Yu, Y Li, X Li, T Jiang, Q Wu, ... Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 102 | 2023 |
| Hyperspectral classification based on texture feature enhancement and deep belief networks J Li, B Xi, Y Li, Q Du, K Wang Remote Sensing 10 (3), 396, 2018 | 101 | 2018 |
| Single image dehazing with a generic model-agnostic convolutional neural network Z Liu, B Xiao, M Alrabeiah, K Wang, J Chen IEEE Signal Processing Letters 26 (6), 833-837, 2019 | 92 | 2019 |
| Dw-gan: A discrete wavelet transform gan for nonhomogeneous dehazing M Fu, H Liu, Y Yu, J Chen, K Wang Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 89 | 2021 |
| A two-branch neural network for non-homogeneous dehazing via ensemble learning Y Yu, H Liu, M Fu, J Chen, X Wang, K Wang Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2021 | 79 | 2021 |
| Underwater image restoration based on a parallel convolutional neural network K Wang, Y Hu, J Chen, X Wu, X Zhao, Y Li Remote sensing 11 (13), 1591, 2019 | 58 | 2019 |
| Background learning based on target suppression constraint for hyperspectral target detection W Xie, X Zhang, Y Li, K Wang, Q Du IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2020 | 51 | 2020 |
| Target detection with unconstrained linear mixture model and hierarchical denoising autoencoder in hyperspectral imagery Y Li, Y Shi, K Wang, B Xi, J Li, P Gamba IEEE Transactions on Image Processing 31, 1418-1432, 2022 | 45 | 2022 |
| Contrastive semi-supervised learning for underwater image restoration via reliable bank S Huang, K Wang, H Liu, J Chen, Y Li Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2023 | 44 | 2023 |
| Underwater image restoration based on convolutional neural network Y Hu, K Wang, X Zhao, H Wang, Y Li Asian conference on machine learning, 296-311, 2018 | 41 | 2018 |
| Towards multi-domain single image dehazing via test-time training H Liu, Z Wu, L Li, S Salehkalaibar, J Chen, K Wang Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2022 | 38 | 2022 |
| Deep residual learning for boosting the accuracy of hyperspectral pansharpening Y Zheng, J Li, Y Li, K Cao, K Wang IEEE Geoscience and Remote Sensing Letters 17 (8), 1435-1439, 2019 | 35 | 2019 |
| Deep fully convolutional regression networks for single image haze removal X Zhao, K Wang, Y Li, J Li 2017 IEEE Visual Communications and Image Processing (VCIP), 1-4, 2017 | 31 | 2017 |
| Deep residual network-based fusion framework for hyperspectral and LiDAR data C Ge, Q Du, W Sun, K Wang, J Li, Y Li IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2021 | 23 | 2021 |
| Generic model-agnostic convolutional neural network for single image dehazing Z Liu, B Xiao, M Alrabeiah, K Wang, J Chen arXiv preprint arXiv:1810.02862, 2018 | 12 | 2018 |
| Can gray code improve the performance of distributed video coding? J Song, P Shen, K Wang, L Zhang, H Song IEEE Access 4, 4431-4441, 2016 | 11 | 2016 |
| Progressive correlation noise refinement for transform domain Wyner-Ziv video coding J Song, K Wang, H Liu, Y Li, C Wu 2011 18th IEEE International Conference on Image Processing, 2625-2628, 2011 | 11 | 2011 |
| Quantitative performance evaluation for dehazing algorithms on synthetic outdoor hazy images K Wang, H Wang, Y Li, Y Hu, Y Li IEEE Access 6, 20481-20496, 2018 | 10 | 2018 |
| Efficient FPGA implementation of modified DWT for JPEG2000 J Guo, K Wang, C Wu, Y Li 2008 9th International Conference on Solid-State and Integrated-Circuit …, 2008 | 10 | 2008 |
| Hyperspectral and LiDAR data fusion classification using superpixel segmentation-based local pixel neighborhood preserving embedding Y Li, C Ge, W Sun, J Peng, Q Du, K Wang Remote Sensing 11 (5), 550, 2019 | 9 | 2019 |
